Module: Single-Cell Analysis

Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies and rare transcript studies to examine cell heterogeneity within a populations of cells. The cellular resolution and genome wide scope make it possible to draw new conclusions that are not otherwise possible with bulk RNA-seq. Slides created by Mehmet Tekman.

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• Slack: #single-cell_scrna-intro
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• To understand the pitfalls in scRNA-seq sequencing and amplification, and how they are overcome.
• Know the types of variation in an analysis and how to control for them.
• Grasp what dimension reduction is, and how it might be performed.
• Be familiarised with the main types of clustering techniques and when to use them.

You’ve done all the work to make a single cell matrix, with gene counts and mitochondrial counts and buckets of cell metadata from all your variables of interest, now it’s time to fully process our data, to remove low quality cells, to reduce the many dimensions of data that make it difficult to work with, and ultimately to try to define our clusters and to find our biological meaning and insights!

• Tutorial: Version in Video | Latest Version
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• Slack: #single-cell_scrna-case_basic-pipeline
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• Interpret quality control plots to direct parameter decisions
• Repeat analysis from matrix to clustering
• Identify decision-making points
• Appraise data outputs and decisions
• Explain why single cell analysis is an iterative (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly) process

This tutorial demonstrates the what barcodes in your sequencing data are, and how to handle them during analysis. Please work through the full tutorial yourself to get all the details.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #single-cell_scrna-umis
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• Demultiplex FASTQ data via UMI-tools
• Understand and validate the extraction of barcodes

This tutorial will take you from raw FASTQ files to a cell x gene data matrix in AnnData format. What’s a data matrix, and what’s AnnData format? Well you’ll find out! Importantly, this is the first step in processing single cell data in order to start analysing it.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #single-cell_scrna-case_alevin
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• Generate a cellxgene matrix for droplet-based single cell sequencing data
• Interpret quality control (QC) plots to make informed decisions on cell thresholds
• Find relevant information in GTF files for the particulars of their study, and include this in data matrix metadata

You’ve done all the work to make a single cell matrix, with gene counts and mitochondrial counts and buckets of cell metadata from all your variables of interest, now it’s time to fully process our data, to remove low quality cells, to reduce the many dimensions of data that make it difficult to work with, and ultimately to try to define our clusters and to find our biological meaning and insights!

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #single-cell_scrna-case_basic-pipeline
  • Slack Invite
• Gitter
• Galaxy Help Forum

• Interpret quality control plots to direct parameter decisions
• Repeat analysis from matrix to clustering
• Identify decision-making points
• Appraise data outputs and decisions
• Explain why single cell analysis is an iterative (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly) process

Bulk RNA-seq data contains a mixture of transcript signatures from several types of cells. We wish to deconvolve this mixture to obtain estimates of the proportions of cell types within the bulk sample. To do this, we can use single cell RNA-seq data as a reference for estimating the cell type proportions within the bulk data.

In this tutorial, we will use bulk and single-cell RNA-seq data, including matrices of similar tissues from different sources, to illustrate how to infer cell type abundances from bulk RNA-seq.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #single-cell_bulk-music
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• Construct Bulk and scRNA Expression Set Objects
• Inspect these objects for various properties
• Measure the abundance of certain cell type cluster markers compared to others